Canek integration process of WT/KO_spleen
Sun
2022-08-10
library(tidyverse)Warning: package ‘tidyverse’ was built under R version 4.1.2
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0.9000
✔ readr 2.1.2 ✔ forcats 0.5.1 Warning: package ‘ggplot2’ was built under R version 4.1.2
Warning: package ‘tibble’ was built under R version 4.1.2
Warning: package ‘tidyr’ was built under R version 4.1.2
Warning: package ‘readr’ was built under R version 4.1.2
Warning: package ‘dplyr’ was built under R version 4.1.2── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(Seurat)Warning: package ‘Seurat’ was built under R version 4.1.2
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Attaching splibrary(Canek)Warning: package ‘Canek’ was built under R version 4.1.2library(scmisc)Warning: replacing previous import ‘Matrix::expand’ by ‘tidyr::expand’ when loading ‘scmisc’
Warning: replacing previous import ‘Matrix::pack’ by ‘tidyr::pack’ when loading ‘scmisc’
Warning: replacing previous import ‘Matrix::unpack’ by ‘tidyr::unpack’ when loading ‘scmisc’
Warning: replacing previous import ‘Matrix::head’ by ‘utils::head’ when loading ‘scmisc’
Warning: replacing previous import ‘Matrix::tail’ by ‘utils::tail’ when loading ‘scmisc’
Attaching package: ‘scmisc’
The following object is masked from ‘package:Seurat’:
as.CellDataSetlibrary(slingshot)Warning: package ‘slingshot’ was built under R version 4.1.3
Loading required package: princurve
Loading required package: TrajectoryUtils
Warning: package ‘TrajectoryUtils’ was built under R version 4.1.1
Loading required package: SingleCellExperiment
Warning: package ‘SingleCellExperiment’ was built under R version 4.1.1
Loading required package: SummarizedExperiment
Warning: package ‘SummarizedExperiment’ was built under R version 4.1.1
Loading required package: MatrixGenerics
Warning: package ‘MatrixGenerics’ was built under R version 4.1.1
Loading required package: matrixStats
Warning: package ‘matrixStats’ was built under R version 4.1.2
Attaching package: ‘matrixStats’
The following object is masked from ‘package:dplyr’:
count
Attaching package: ‘MatrixGenerics’
The following objects are masked from ‘package:matrixStats’:
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Warning: package ‘GenomicRanges’ was built under R version 4.1.2
Loading required package: stats4
Loading required package: BiocGenerics
Warning: package ‘BiocGenerics’ was built under R version 4.1.1
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get,
grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position,
rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Warning: package ‘S4Vectors’ was built under R version 4.1.3
Attaching package: ‘S4Vectors’
The following objects are masked from ‘package:dplyr’:
first, rename
The following object is masked from ‘package:tidyr’:
expand
The following objects are masked from ‘package:base’:
expand.grid, I, unname
Loading required package: IRanges
Warning: package ‘IRanges’ was built under R version 4.1.1
Attaching package: ‘IRanges’
The following object is masked from ‘package:sp’:
%over%
The following objects are masked from ‘package:dplyr’:
collapse, desc, slice
The following object is masked from ‘package:purrr’:
reduce
Loading required package: GenomeInfoDb
Warning: package ‘GenomeInfoDb’ was built under R version 4.1.2
Loading required package: Biobase
Warning: package ‘Biobase’ was built under R version 4.1.1
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for
packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
The following object is masked from ‘package:MatrixGenerics’:
rowMedians
The following objects are masked from ‘package:matrixStats’:
anyMissing, rowMedians
Attaching package: ‘SummarizedExperiment’
The following object is masked from ‘package:SeuratObject’:
Assays
The following object is masked from ‘package:Seurat’:
Assayslibrary(singleCellHaystack)
set.seed(1)
theme_set(cowplot::theme_cowplot())# font_size font_familylive spleen
x <- list(
KO_spleen = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/KO_live_spleen_process.rds"),
WT_spleen = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_live_spleen_process.rds")
)This might not be needed if info already there. x[[“WT”]]\(background <- "WT" x[["KO"]]\)background <- “KO”
Naive merge
x <- Reduce(merge, x)x <- NormalizeData(x)
x <- FindVariableFeatures(x, nfeatures = 5000)
VariableFeaturePlot(x)
x <- ScaleData(x)
# x <- lapply(x, FindVariableFeatures, nfeatures=5000)x <- RunPCA(x)
ElbowPlot(x, ndims=50)x <- RunUMAP(x, dims = 1:20)
DimPlot(x, group.by = "background")
plot_coord(x, expand = "background")x <- FindNeighbors(x, dims = 1:20)
x <- FindClusters(x, algorithm = "leiden", res = .6)DimHeatmap(x, dims = 1:6)DimPlot(x, label = TRUE) + NoLegend()
DimPlot(x, group.by = "background")
DimPlot(x, label = TRUE, split.by = "background", pt.size=0.01, order = FALSE)x$background <- factor(x=x$background, levels = c("WT_spleen","KO_spleen"))Canek
set.seed(1)#x <- list(
# WT = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/1.process/WT_NK_process_NKonly.rds"),
# KO = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/1.process/KO_NK_process_NKonly.rds")
#)x <- SplitObject(x, "background")
# x <- FindVariableFeatures(x, nfeatures = 2000)
x <- lapply(x, FindVariableFeatures, nfeatures = 5000) # adjust nfeatures as needed.Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|# VariableFeaturePlot(x)
# plot_loadingss=(x, head(VariableFeatures(x), 20))x <- RunCanek(x,nfeature=5000)x <- ScaleData(x)Centering and scaling data matrix
|
| | 0%
|
|============================ | 20%
|
|======================================================= | 40%
|
|=================================================================================== | 60%
|
|============================================================================================================== | 80%
|
|==========================================================================================================================================| 100%x <- RunPCA(x)PC_ 1
Positive: Plbd1, Ifitm3, Spi1, H2-Ab1, H2-Aa, H2-Eb1, Ly86, Alox5ap, Cd74, Ifitm2
Cd300a, Cybb, Ifitm6, Cd209a, Tifab, Pirb, Rnase6, Cd300c2, H2-DMb1, Ctsh
Pld4, Wfdc17, Lrrc25, Pid1, Ppfia4, Clec12a, Hck, Rassf4, Flt3, Ccl6
Negative: Ccl5, Gzma, Prf1, S1pr5, Ncr1, Lgals1, S100a10, Gzmb, H2afz, Cma1
Tyrobp, Anxa2, Klrg1, Klra8, Sytl3, Klra9, Tbx21, Klrc2, Serpinb9, Ahnak
Cd9, Zeb2, Klra4, Dusp2, Il2rb, Il18rap, Id2, Gm19585, Borcs7, Klra3
PC_ 2
Positive: Gzma, Tyrobp, Plbd1, Ifitm3, Spi1, Alox5ap, Prf1, Ly86, Cd300a, S1pr5
Ifitm2, Ifitm6, Cma1, Lgals1, Cybb, Cd209a, Tifab, Gzmb, Cd300c2, Pirb
Wfdc17, Ctsh, Pld4, Anxa2, Pid1, Ccl6, Zeb2, H2afz, Rassf4, Rnase6
Negative: Ltb, Rps20, Rps15a, Rpl13, Rplp1, Il7r, Rpsa, Cd3d, Rpl23, Rpl12
Rps2, Rplp0, Rps5, Rps7, Rps19, Rps18, Rpl32, Cd3g, Thy1, Rpl18
Rps13, Rps4x, Cd3e, Rps8, Rpl10a, Rpl36a, Rpl17, Rpl39, Rps28, Ly6e
PC_ 3
Positive: Cd3e, Cd3d, Cd3g, Cd8b1, Il7r, Ly6c2, Cd8a, Gramd3, Klk8, Trac
Slfn1, Cd6, Trbv13-2, Cd5, Trgv2, Sit1, Cxcr6, Lat, Ltb, Nsg2
Thy1, Bcl11b, Rgs10, Gm8369, Themis, Gpr183, Sh3bp5, Rps13, Adam19, Trbv12-2
Negative: Pclaf, Birc5, Spc24, Ccna2, Mki67, Cdca3, Top2a, Ccnb2, Cdca8, Rrm2
Ube2c, Nusap1, Cks1b, Tpx2, Cdk1, Aurkb, Shcbp1, Plk1, Neil3, Hist1h1b
Kif11, Cenpf, Clspn, Tk1, Hmmr, Fbxo5, Cenpm, Cdca5, Ckap2l, Esco2
PC_ 4
Positive: Cd3e, Cd3g, Cd3d, Pclaf, Birc5, Ccna2, Cd8b1, Spc24, Ccnb2, Cdca3
Mki67, Cdca8, Nusap1, Rrm2, Tpx2, Ube2c, Cdk1, Cd8a, Hist1h1b, Plk1
Neil3, Hmmr, Shcbp1, Top2a, Cenpf, Kif11, Trac, Ly6c2, Aurkb, Fbxo5
Negative: Tyrobp, Ncr1, Klra4, Klra8, Ctla2a, Klrb1f, Rpl10, Il2rb, Klri2, Sell
Eomes, Klrc2, Xcl1, Ccr2, Serpina3g, Car2, Itgax, Rps11, Klrb1a, Kit
Cd9, Ccl3, Ccr5, Jun, Fos, Sytl3, Irf8, Cd244, Ctla2b, Serpinb6b
PC_ 5
Positive: Ckb, Tmem176b, Asb2, Tmem176a, Cxcr6, AC163354.1, Podnl1, Tnfrsf25, Tcrg-C4, Rora
Il2ra, Cd226, Id2, Ramp1, Serpina3f, Maf, Cd81, Adam8, Fam184b, Itm2c
Tnf, Cd7, Dll1, Cd160, Inpp4b, Ly6a, Il7r, Dtx1, Gm14718, Serpinb1a
Negative: Rpl32, Rps5, Sell, Rpsa, Rps4x, Rplp0, Rpl39, Rps15a, Rps7, Rps8
Rpl23, Rpl41, Rpl13, Ctla2a, Rpl18, Rps3, Rpl8, Rpl28, Rps26, Rps13
Rps11, Rps20, Rpl6, Rpl10a, Rps28, Rps12, Rpl17, Rpl7, H2afz, Rpl15 ElbowPlot(x, ndims=50)
DimPlot(x, group.by="background")
DimHeatmap(x, dims=1:6)
x <- RunUMAP(x, dims = 1:20)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
14:36:25 UMAP embedding parameters a = 0.9922 b = 1.112
14:36:25 Read 19828 rows and found 20 numeric columns
14:36:25 Using Annoy for neighbor search, n_neighbors = 30
14:36:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:36:26 Writing NN index file to temp file /var/folders/gw/069y9h752hdbn01wcy57ktj00000gn/T//RtmpMADga3/fileb17972f02fcc
14:36:27 Searching Annoy index using 1 thread, search_k = 3000
14:36:32 Annoy recall = 100%
14:36:32 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
14:36:34 Initializing from normalized Laplacian + noise (using irlba)
14:36:35 Commencing optimization for 200 epochs, with 843636 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:36:45 Optimization finishedx <- FindNeighbors(x, dims = 1:20)Computing nearest neighbor graph
Computing SNNx <- FindClusters(x, algorithm = "leiden", res = .6)# x$background <- factor(x=x$background, levels = c("WT_NK","KO_NK"))DimPlot(x, label = TRUE)
x$background <- factor(x=x$background, levels = c("WT_spleen","KO_spleen"))DimPlot(x, split.by="background", label = TRUE) + NoLegend()+
ggtitle("Canek integration WT&KO_spleen (nfeatures=5000)")+
theme(plot.title = element_text(size=13, face = "bold.italic"))Export
write_rds(x, "/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen.rds")y,sampling WT & KO
x<- read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen.rds")DimPlot(x, label = TRUE)
y <- sample_cells(x, group="background")Neither 'n', 'frac' or 'n_max' specified. Sampling 8016 cells per group.table(y$background)
WT_spleen KO_spleen
8016 8016 DimPlot(y, label = TRUE)+ NoLegend()
DimPlot(y, split.by="background", label = TRUE) + NoLegend()+
ggtitle("Canek integration of WT/KO_live spleen (nfeatures=5000)")+
theme(plot.title = element_text(size=13, face = "bold.italic"))
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/featureplot.svg", width=10, heigh=6)DimPlot(y, split.by="background") +
ggtitle("Canek integration of WT/KO_live spleen (nfeatures=5000)")+
theme(plot.title = element_text(size=13, face = "bold.italic"))
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/live_spleen_featureplot_nolabel.svg", width=10, heigh=6)p <- c("NK1.1", "NKP46", "CD49B", "CD27", "CD11B")
g <- c("Klrb1", "Ncr1", "Itgam", "Cd27", "Itga2")FeaturePlot(y, c(p, g), order = TRUE,label=TRUE, ncol = 5) & NoLegend()Warning: Could not find NK1.1 in the default search locations, found in ADT assay instead
Warning: Could not find NKP46 in the default search locations, found in ADT assay instead
Warning: Could not find CD49B in the default search locations, found in ADT assay instead
Warning: Could not find CD27 in the default search locations, found in ADT assay instead
Warning: Could not find CD11B in the default search locations, found in ADT assay instead
FeaturePlot(y, "Pou2f2", order = TRUE, label = TRUE, label.size = 3.5)
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/pou2f2_featureplot.svg", width=8, heigh=6)FeaturePlot(y, "Gzmk", order = TRUE, label = TRUE, label.size = 3.5)
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Gzmk_featureplot.svg", width=8, heigh=6)FeaturePlot(y, "Ccr2", order = TRUE, label = TRUE, label.size = 3.5)ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr2_featureplot.svg", width=8, heigh=6)FeaturePlot(y, "Klrg1", order = TRUE, label = TRUE, label.size = 3.5)
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Klrg1_featureplot.svg", width=8, heigh=6)FeaturePlot(y, "Ccr7", order = TRUE, label = TRUE, label.size = 3.5)
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr7_featureplot.svg", width=8, heigh=6)VlnPlot(y, features = "Ccr7",pt.size=0.05, split.by = "background")ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr7_vlnplot.svg", width=8, heigh=5)VlnPlot(y, features = "Pou2f2",pt.size=0.05, split.by = "background")ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/pou2f2_vlnplot.svg", width=8, heigh=5)VlnPlot(y, features = "Gzmk",pt.size=0.05, split.by = "background")ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Gzmk_vlnplot.svg", width=8, heigh=5)VlnPlot(y, features = "Ccr2",pt.size=0.05, split.by = "background")ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr2_vlnplot.svg", width=8, heigh=5)VlnPlot(y, features = "Klrg1",pt.size=0.05, split.by = "background")ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Klrg1_vlnplot.svg", width=8, heigh=5)ggplot(y[[]], aes(seurat_clusters, fill = background)) +
geom_bar(position = "fill")
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/barplot1.svg", width=10, heigh=7)ggplot(y[[]], aes(seurat_clusters, fill = background)) +
geom_bar(position = "dodge")
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/barplot2.svg", width=10, heigh=7)# FeaturePlot(y, c("Pou2f2","Gzmk"),min.cutoff = "q05", max.cutoff = "q95", order = TRUE, label = TRUE)VlnPlot(y, features = c(p,g),pt.size=0.1, ncol = 5)# DotPlot(y, features= g, dot.scale=6)+ RotatedAxis()+
# scale_color_distiller(palette="RdBu")check correlation
z <- y[,y$seurat_clusters=="3"]DimPlot(z, split.by="background", label = TRUE) + NoLegend()+
theme(plot.title = element_text(size=13, face = "bold.italic"))
- Gzmk and Pou2f2
FeatureScatter(z, "Gzmk", "Pou2f2")
# z -> cluster 3
FeatureScatter(y, "Gzmk", "Pou2f2")
- Ifng and Pou2f2
FeatureScatter(z, "Ifng", "Pou2f2")
#+ggtitle("Correlation between Ifng and Pou2f2 in live_spleen cluster 3")FeatureScatter(y, "Ifng", "Pou2f2")
Find all markers
deg <- FindAllMarkers(y,max.cells.per.ident = 500, verbose = TRUE)Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~05s
|+ | 2 % ~03s
|++ | 3 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~00s
|++ | 3 % ~00s
|+++ | 4 % ~00s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 9 % ~00s
|++++++ | 10% ~00s
|++++++ | 12% ~00s
|+++++++ | 13% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 16% ~00s
|+++++++++ | 18% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++ | 24% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 27% ~00s
|+++++++++++++++ | 28% ~00s
|+++++++++++++++ | 30% ~00s
|++++++++++++++++ | 31% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 34% ~00s
|++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 37% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 6 % ~00s
|++++ | 7 % ~00s
|+++++ | 8 % ~00s
|+++++ | 10% ~00s
|++++++ | 11% ~00s
|+++++++ | 12% ~00s
|+++++++ | 14% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 18% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 21% ~00s
|++++++++++++ | 22% ~00s
|++++++++++++ | 24% ~00s
|+++++++++++++ | 25% ~00s
|++++++++++++++ | 26% ~00s
|++++++++++++++ | 28% ~00s
|+++++++++++++++ | 29% ~00s
|++++++++++++++++ | 31% ~00s
|++++++++++++++++ | 32% ~00s
|+++++++++++++++++ | 33% ~00s
|++++++++++++++++++ | 35% ~00s
|+++++++++++++++++++ | 36% ~00s
|+++++++++++++++++++ | 38% ~00s
|++++++++++++++++++++ | 39% ~00s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 42% ~00s
|++++++++++++++++++++++ | 43% ~00s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 6
| | 0 % ~calculating
|+ | 2 % ~00s
|++ | 4 % ~00s
|+++ | 6 % ~00s
|++++ | 8 % ~00s
|+++++ | 10% ~00s
|++++++ | 12% ~00s
|+++++++ | 13% ~00s
|++++++++ | 15% ~00s
|+++++++++ | 17% ~00s
|++++++++++ | 19% ~00s
|+++++++++++ | 21% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|+++++++++++++++++++++ | 40% ~02s
|++++++++++++++++++++++ | 42% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|++++++++++++++++++++++++ | 46% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 23% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|++++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~08s
|+++++ | 8 % ~07s
|+++++ | 9 % ~06s
|++++++ | 10% ~06s
|++++++ | 11% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|+++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 5 % ~01s
|+++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|+++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 43% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 13
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~01s
|++ | 3 % ~02s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 14
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 15
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 8 % ~01s
|+++++ | 9 % ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|+++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|++++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|+++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|+++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|+++++++++++++++++++++++++++++ | 58% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|+++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 16
| | 0 % ~calculating
|+ | 1 % ~01s
|++ | 2 % ~01s
|++ | 3 % ~01s
|+++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 7 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|++++++ | 12% ~01s
|+++++++ | 13% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|+++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~00s
|+++++++++++++++++++++ | 41% ~00s
|++++++++++++++++++++++ | 43% ~00s
|++++++++++++++++++++++ | 44% ~00s
|+++++++++++++++++++++++ | 45% ~00s
|+++++++++++++++++++++++ | 46% ~00s
|++++++++++++++++++++++++ | 47% ~00s
|++++++++++++++++++++++++ | 48% ~00s
|+++++++++++++++++++++++++ | 49% ~00s
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++ | 51% ~00s
|+++++++++++++++++++++++++++ | 52% ~00s
|+++++++++++++++++++++++++++ | 53% ~00s
|++++++++++++++++++++++++++++ | 54% ~00s
|++++++++++++++++++++++++++++ | 55% ~00s
|+++++++++++++++++++++++++++++ | 56% ~00s
|+++++++++++++++++++++++++++++ | 57% ~00s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|+++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 63% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|+++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 67% ~00s
|+++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|++++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 17
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~03s
|++ | 3 % ~02s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s #每一个cluster高表达的gene和低表达的gene
deghead(deg)plot_volcano(deg, n=20, fdr=0.05)Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider increasing max.overlaps
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/volcano.svg", width=20, heigh=15)top <- deg%>%filter(p_val_adj<0.01, abs(avg_log2FC)>1)
toptop_genes <- top_deg(deg)
top_genesz <-sample_cells(y, group = "seurat_clusters")Neither 'n', 'frac' or 'n_max' specified. Sampling 19 cells per group.p<- plot_heatmap(z[top_genes$gene,], column_split= z$seurat_clusters)
p
p<- ggplotify::as.ggplot(plot_heatmap(z[top_genes$gene,], column_split= z$seurat_clusters))
p
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/heatmap.svg", width=22, height=15)y export
write_rds(y, "/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen_sampling.rds")---
title: "Canek integration process of WT/KO_spleen"
author: "Sun"
date: "2022-08-10"
output: html_notebook
---

```{r setup}
library(tidyverse)
library(Seurat)
library(Canek)
library(scmisc)
library(slingshot)
library(singleCellHaystack)
set.seed(1)

theme_set(cowplot::theme_cowplot())# font_size  font_family
```

# live spleen 
```{r}
x <- list(
  KO_spleen = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/KO_live_spleen_process.rds"),
  WT_spleen = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_live_spleen_process.rds")
)
```

This might not be needed if info already there.
x[["WT"]]$background <- "WT"
x[["KO"]]$background <- "KO"


## Naive merge 

```{r}
x <- Reduce(merge, x)
```


```{r}
x <- NormalizeData(x)
x <- FindVariableFeatures(x, nfeatures = 5000)
VariableFeaturePlot(x)
x <- ScaleData(x)
# x <- lapply(x, FindVariableFeatures, nfeatures=5000)
```

```{r}
x <- RunPCA(x)
ElbowPlot(x, ndims=50)
```

```{r}
x <- RunUMAP(x, dims = 1:20)
DimPlot(x, group.by = "background")
plot_coord(x, expand = "background")
```

```{r warning=FALSE}
x <- FindNeighbors(x, dims = 1:20)
x <- FindClusters(x, algorithm = "leiden", res = .6)
```

```{r}
DimHeatmap(x, dims = 1:6)
```

```{r}
DimPlot(x, label = TRUE) + NoLegend()
DimPlot(x, group.by = "background")
DimPlot(x, label = TRUE, split.by = "background", pt.size=0.01, order = FALSE)
```

```{r}
x$background <- factor(x=x$background, levels = c("WT_spleen","KO_spleen"))
```

## Canek 

```{r}
set.seed(1)
```

```{r}
#x <- list(
 # WT = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/1.process/WT_NK_process_NKonly.rds"),
#  KO = read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/1.process/KO_NK_process_NKonly.rds")
#)
```

```{r}
x <- SplitObject(x, "background")
# x <- FindVariableFeatures(x, nfeatures = 2000)
x <- lapply(x, FindVariableFeatures, nfeatures = 5000) # adjust nfeatures as needed.
```

```{r}
# VariableFeaturePlot(x)
# plot_loadingss=(x, head(VariableFeatures(x), 20))
```

```{r}
x <- RunCanek(x,nfeature=5000)
```

```{r}
x <- ScaleData(x)
```

```{r}
x <- RunPCA(x)
ElbowPlot(x, ndims=50)
```

```{r}
DimPlot(x, group.by="background")
```

```{r}
DimHeatmap(x, dims=1:6)
```


```{r}
x <- RunUMAP(x, dims = 1:20)
```

```{r warning=FALSE}
x <- FindNeighbors(x, dims = 1:20)
x <- FindClusters(x, algorithm = "leiden", res = .6)
```

```{r}
# x$background <- factor(x=x$background, levels = c("WT_NK","KO_NK"))
```


```{r}
DimPlot(x, label = TRUE)
```

```{r}
x$background <- factor(x=x$background, levels = c("WT_spleen","KO_spleen"))
```

```{r}
DimPlot(x, split.by="background", label = TRUE) + NoLegend()+
   ggtitle("Canek integration WT&KO_spleen (nfeatures=5000)")+
  theme(plot.title = element_text(size=13, face = "bold.italic"))
```

## Export 

```{r}
write_rds(x, "/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen.rds")
```

## y,sampling WT & KO

```{r}
x<- read_rds("/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen.rds")
```

```{r}
DimPlot(x, label = TRUE)
```


```{r}
y <- sample_cells(x, group="background")
table(y$background)
```

```{r}
DimPlot(y, label = TRUE)+ NoLegend()
```




```{r}
DimPlot(y, split.by="background", label = TRUE) + NoLegend()+
   ggtitle("Canek integration of WT/KO_live spleen (nfeatures=5000)")+
  theme(plot.title = element_text(size=13, face = "bold.italic"))
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/featureplot.svg", width=10, heigh=6)
```

```{r}
DimPlot(y, split.by="background") + 
   ggtitle("Canek integration of WT/KO_live spleen (nfeatures=5000)")+
  theme(plot.title = element_text(size=13, face = "bold.italic"))
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/live_spleen_featureplot_nolabel.svg", width=10, heigh=6)
```




```{r}
p <- c("NK1.1", "NKP46", "CD49B", "CD27", "CD11B")
g <- c("Klrb1", "Ncr1", "Itgam", "Cd27", "Itga2")
```

```{r, fig.width=8, fig.height=3}
FeaturePlot(y, c(p, g), order = TRUE,label=TRUE, ncol = 5) & NoLegend()
```

```{r}
FeaturePlot(y, "Pou2f2", order = TRUE, label = TRUE, label.size = 3.5)
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/pou2f2_featureplot.svg", width=8, heigh=6)
```


```{r}
FeaturePlot(y, "Gzmk", order = TRUE, label = TRUE, label.size = 3.5)
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Gzmk_featureplot.svg", width=8, heigh=6)
```




```{r}
FeaturePlot(y, "Ccr2", order = TRUE, label = TRUE, label.size = 3.5)
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr2_featureplot.svg", width=8, heigh=6)
```

```{r}
FeaturePlot(y, "Klrg1", order = TRUE, label = TRUE, label.size = 3.5)
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Klrg1_featureplot.svg", width=8, heigh=6)
```


```{r}
FeaturePlot(y, "Ccr7", order = TRUE, label = TRUE, label.size = 3.5)
```


```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr7_featureplot.svg", width=8, heigh=6)
```


```{r}
VlnPlot(y, features = "Ccr7",pt.size=0.05, split.by = "background")
```
```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr7_vlnplot.svg", width=8, heigh=5)
```

```{r}
VlnPlot(y, features = "Pou2f2",pt.size=0.05, split.by = "background")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/pou2f2_vlnplot.svg", width=8, heigh=5)
```


```{r}
VlnPlot(y, features = "Gzmk",pt.size=0.05, split.by = "background")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Gzmk_vlnplot.svg", width=8, heigh=5)
```


```{r}
VlnPlot(y, features = "Ccr2",pt.size=0.05, split.by = "background")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Ccr2_vlnplot.svg", width=8, heigh=5)
```

```{r}
VlnPlot(y, features = "Klrg1",pt.size=0.05, split.by = "background")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/Klrg1_vlnplot.svg", width=8, heigh=5)
```



```{r}
ggplot(y[[]], aes(seurat_clusters, fill = background)) +
  geom_bar(position = "fill")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/barplot1.svg", width=10, heigh=7)
```

```{r}
ggplot(y[[]], aes(seurat_clusters, fill = background)) +
  geom_bar(position = "dodge")
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/barplot2.svg", width=10, heigh=7)
```




```{r}
# FeaturePlot(y, c("Pou2f2","Gzmk"),min.cutoff = "q05", max.cutoff = "q95", order = TRUE, label = TRUE)
```

```{r, fig.width=8, fig.height=3}
VlnPlot(y, features = c(p,g),pt.size=0.1, ncol = 5)
```

```{r}
# DotPlot(y, features= g, dot.scale=6)+ RotatedAxis()+
#  scale_color_distiller(palette="RdBu")
```


## check correlation 

```{r}
z <- y[,y$seurat_clusters=="3"]
```

```{r}
DimPlot(z, split.by="background", label = TRUE) + NoLegend()+
  theme(plot.title = element_text(size=13, face = "bold.italic"))
```

- Gzmk and Pou2f2

```{r}
FeatureScatter(z, "Gzmk", "Pou2f2")

# z -> cluster 3 

```

```{r}
FeatureScatter(y, "Gzmk", "Pou2f2")
```

- Ifng and Pou2f2 

```{r}
FeatureScatter(z, "Ifng", "Pou2f2")
#+ggtitle("Correlation between Ifng and Pou2f2 in live_spleen cluster 3")
```

```{r}
FeatureScatter(y, "Ifng", "Pou2f2")

```


## Find all markers 

```{r}
deg <- FindAllMarkers(y,max.cells.per.ident = 500, verbose = TRUE)
#每一个cluster高表达的gene和低表达的gene
deg
head(deg)
```

```{r,fig.width=15, fig.height=10}
plot_volcano(deg, n=20, fdr=0.05)
```

```{r}
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/volcano.svg", width=20, heigh=15)
```

```{r}
top <- deg%>%filter(p_val_adj<0.01, abs(avg_log2FC)>1)
top
```

```{r}
top_genes <- top_deg(deg)
top_genes
```

```{r}
z <-sample_cells(y, group = "seurat_clusters")
```

```{r, fig.width=15, fig.height=10}
p<- plot_heatmap(z[top_genes$gene,], column_split= z$seurat_clusters)
p
```


```{r}
p<- ggplotify::as.ggplot(plot_heatmap(z[top_genes$gene,], column_split= z$seurat_clusters))
p
ggsave("/Users/sunxin/Documents/Projects/submission/Figs/Fig.2_singlecell/live_spleen/heatmap.svg", width=22, height=15)
```


## y export

```{r}
write_rds(y, "/Users/sunxin/Documents/Projects/NKsc_tumor/analysis_in_R/4.spleen_TIL_integration/output/WT_KO_spleen_integration_process_livespleen_sampling.rds")
```



